Item-Based and User-Based Incremental Collaborative Filtering for Web Recommendations

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Catarina Miranda (Author) Other; Alipio Mario Jorge (Author) FCUP

Conference proceedings International

Title: Progress in Artificial Intelligence: Proceedings of the 14th Portuguese Conference on Artificial Intelligence, EPIA2009

Pages: 673-684

14th EPIA - Portuguese Conference on Artificial Intelligence - EPIA 2009

Universidade de Aveiro, Portugal, 12 a 15 de Outubro de 2009

Indexing

ISI Web of Knowledge - 10 Citations

Scopus - 17 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-003-RWH

DOI: 10.1007/978-3-642-04686-5_55

Abstract (EN): In this paper we propose an incremental item-based collaborative filtering algorithm. It works with binary ratings (sometimes also called implicit ratings), as it; is typically the case in a Web environment. Our method is capable of incorporating new information in parallel with performing recommendation. New sessions and new users are used to update the similarity matrix as they appear. The proposed algorithm is compared with a non-incremental one, as well as with an incremental user-based approach, based oil an existing explicit, rating recommender. The use of techniques for working with sparse matrices oil these algorithms is also evaluated. All versions, implemented ill R, are evaluated on 5 datasets with various number of users and/or items. We observed that: Recall tends to improve when we continuously add information to the recommender model; the time spent for recommendation does not degrade; the time for updating the similarity matrix (necessary to the recommendation) is relatively low and motivates the use of the item-based incremental approach. Moreover we study how the number of items and users affects the user based and the item based approaches.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12